Smart Holster Autonomous Alert System for Wearable Emergency Responders
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Solution Overview
Problem
Law enforcement officers often face situations where they cannot manually call for backup or report events due to being incapacitated or in stealthy situations, leading to increased assaults, injuries, and deaths, as existing solutions require conscious action from the officer.
Innovation Solution
A wearable emergency responder system that includes a smart holster and peripherals, using sensors and microcontrollers to detect events and automatically send signals to a command center for real-time alert generation and dissemination without requiring conscious action from the officer, utilizing a signal IO module, rule server, and predictive models to process and respond to sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If officers use manual radio communication to report events, then they can convey information about ongoing events, but they cannot report events when incapacitated or required to remain silent
Solution Approach 1:
The wearable system automatically detects events using sensors and triggers alerts without requiring officer intervention. The system serves itself by autonomously monitoring physiological parameters, movement patterns, and environmental data to identify critical situations and communicate them to command centers, eliminating the need for manual radio reporting.
Solution Approach 2:
The patent replaces the mechanical manual radio communication system with an automated electronic monitoring and communication system. Sensors, microcontrollers, and wireless transmitters substitute for the officer's manual radio operation, enabling automatic event detection and reporting through electronic detection and communication mechanisms.
2Productivity
If officers focus fully on law enforcement maneuvers, then they can respond effectively to situations, but they may forget to call for backup or perform critical tasks
Solution Approach 1:
The system provides continuous feedback to command centers about officer status, location, and detected events. Command centers receive real-time data from sensors monitoring the officer's physiological state and environment, enabling automated feedback loops that trigger appropriate responses such as dispatching backup or alerting medical personnel without requiring officer cognitive resources.
Solution Approach 2:
The automated monitoring system performs the critical task of surveillance and event detection without consuming the officer's attention. The system independently monitors for critical events, manages communication protocols, and coordinates with command centers, freeing the officer to focus entirely on law enforcement maneuvers while ensuring critical tasks are not overlooked.
3Reliability
If wearable systems continuously monitor officer status, then they can detect critical events automatically, but they increase device complexity and energy consumption
Solution Approach 1:
The wearable system is divided into separate functional modules: physiological sensors (heart rate, temperature), movement sensors (accelerometers, gyroscopes), environmental sensors, microcontroller units, and wireless communication components. Each module performs a specific function and can be independently optimized, managed, or replaced, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Data Source
AI summary
A system processes a series of incoming message to generate an outgoing message. In exemplary embodiments, the incoming messages comprise a first event from a wearable holster configured to accept a weapon, then receiving a second event from the wearable holster. The first signal and second signal are compared based on their respective content. The received signals derive from sensor data such as a switch, an accelerometer, a GPS sensor, a wrist device, a head device. The comparison invokes additional processing to determine the contents of a message to be sent to at least one recipient. Contents of messages are captured into a learning model, and when comparing contents of the first signal to contents of the second signal comprises the learning model is used to generate a prediction that causes an alert to be emitted.